Research studies in the media industry typically, and consistently, indicate that more than 50% of radio listening is done in a vehicle, such as a car, and further that more than 50% of all audio listening occurs in such a vehicle. However, today there does not exist any ability to actually measure and effectively analyze what people are really listening to, or watching, while in a vehicle—with accurate time and/or location data tied to a distributed ledger.
At the outset, measurement and analysis of media consumption while in a vehicle is important to multiple stakeholders, such as: 1) radio (and other content) advertising businesses to support the buying/selling and pricing of advertising (the US market for radio advertising alone being valued at $17 billion in 2013); 2) radio station owners and programming managers to guide their selection of programming and on-air talent; 3) the music industry to gauge public reaction to artists and their work; 4) the automotive industry to understand the behavior of their customers while in their vehicles; 5) and any other entity that may be interested in the measurement and analysis of such media consumption.
There have been many attempts in the prior art to generate estimates of the use of in-vehicle audio content. For example, Nielsen Audio, previously Arbitron, provides a service to estimate the audience of AM/FM radio stations, which is primarily based on periodic survey methodologies using samples. These include use of log book/diaries, which are manually filled out by a limited sample of selected participants, and the use of specialized devices such as Nielsen's “Portable People Meter” or PPM. A PPM is a small device worn or carried by selected participants which identifies any AM/FM radio stations in earshot of the participant from identification signals embedded in each individual radio station broadcast. Other approaches have involved the use of expensive specialized measurement equipment added to a sample number of vehicles.
However, these existing prior art methods have many disadvantages and problems. As a result, these estimation methodologies are considered to be outdated and inadequate to meet the current needs of stakeholders because they, for example, suffer from: 1) small participant sample size; 2) high cost of gathering data in this manner; 3) potential for the statistical integrity of the approach to be compromised (whether unintentionally or fraudulently) by the participants; 4) infrequent periodic timing (only several times per year) with significant lag time between survey and report availability, thus not providing the potential for real-time monitoring and analysis desired by the industry; 5) lack of ability to comparatively measure “cross channel” audio consumption (e.g. AM/FM radio vs. SDARS vs. Internet Radio, etc.); 6) the lack of ability to measure all types of media consumption (e.g. audio, video, etc.); and at least 7) the lack of the ability to ensure the integrity of the data by immutability tying such data to a distributed ledger schema.
Despite the foregoing limitations in the methodology used, Nielsen still generated more than $450M from the sale of AM/FM radio measurement data for the US market in 2013 as no viable alternative rating source data is available.
Nielsen utilizes panels of selected participants where they ask questions regarding audio usage and then extrapolates to the population. Nielsen also utilizes a PPM (portable people meter) which is a small metering device that is carried by a small group of people which listens to what audio is around them and can identify what stations are playing based on code that is, embedded in a station's broadcast, to measure FM and AM radio. This too is a sample.
As another prior art example, Triton Digital measures Internet radio listening utilizing server logs for each station/channel. Typically, each individual channel has access to this information as well from their content delivery network.
In a further example, SiriusXM is not able to measure what channels its subscribers are listening to as it is primarily a one-way broadcast via satellites,
In view of the above, there is currently no comprehensive source of data for the accurate measurement of the full spectrum of media content that is actually consumed in an automobile. The currently available estimates of in-vehicle audio listening are deficient in many ways, including: 1) Not real-time or near real-time (surveys conducted only several times per year with considerable lag time before reports are available); 2) Do not cover all potential media sources (e.g. can estimate AM/FM radio but cannot estimate SDARS, internet radio, stored media, streaming media, etc.); 3) Unable to provide “cross-channel” comparison (e.g. between FM & SDARS); 4) Unable to measure content brought in to the vehicle via a connected MP3 player, DVD/Blu-ray player, smartphone or other Consumer Electronic (CE) device; 5) Survey-based methodology (rather than actual measurement); 6) Small survey participant sample size; 7) Significant vulnerability to bias and fraud; 8) High cost of data collection (both the high cost of administering the survey participants and the high cost of specialized monitoring equipment such as Nielsen's PPM device); 9) Provide minimal geographic location information; 10) Does not include any accurate timing information for correlation with multiple sources; 11) are unable to provide detailed information on which advertising commercials a user heard, how and where they were heard, and whether the user took action as a result of hearing the ad, etc.
The clear industry requirement, not met by any existing system, is for a comprehensive capability that measures all forms of media consumed in the vehicle including, but not limited to, terrestrial AM/FM, HD Radio, SDARS (SIRIUS XM), Internet radio and audio/video streaming services (e.g. PANDORA, TUNEIN, SPOTIFY, RDIO, SONGZA, YOUTUBE, etc.), personal media collection (CD, MP3, podcast, DVD, Blu-ray, etc.), audio books, podcasts, text-to-speech, use of hands-free calling and other audio, including content routed to the In Vehicle Entertainment (IVE) system through integration with a smartphone, MP3 player or similar external CE device (via wired or wireless connectivity, including but not limited to USB, BLUETOOTH, Wi-Fi, etc. and including various platforms for in-vehicle smartphone integration such as APPLE CARPLAY, GOOGLE ANDROID AUTO, HARMAN AHA RADIO, PANASONIC AUPEO, PIONEER ZYPR, FORD SYNC, MIRRORLINK, AIRBIQUITY CHOREO, etc.).
Another clear requirement, which is not met by any existing system is the need to facilitate low-cost, large-scale deployment to support measurement from a large user sample to ensure a high level of statistical integrity and accuracy. Existing approaches using a) survey-based methodologies or b) methodologies requiring specialized equipment that needs to be installed in a vehicle do not provide the potential to meet this objective in a viable and cost-effective manner.
To meet industry expectation, there is a need for a system to be able to continuously provide measurement data in real-time and with a high degree of geographic location accuracy. A large sample size, as identified above, is also a pre-requisite of achieving this requirement.
Still further, having developed a system and methodology to actually measure the media content, including audio and video, consumed in a vehicle, there is also a demand for a differentiation between multiple users of the vehicle (e.g. members of the same family). This includes contextual analysis of how media consumption may differ with situation (e.g. a mother or father may primarily listen to adult news and music content during their commute while alone in the car but might listen to kids channels whenever their children are in the car).
Further still, the instant system and methodology allows for the measurement of data, audio, and video as well as other content delivered to a vehicle. For example, a vehicle can receive a display ad, coupon, cryptographic token, audio, video or other content relating to a fast food restaurant. Using data associated with what is being broadcast/transmitted to the vehicle as well as location data from the system, the instant method and system can determine whether the vehicle took an action, e.g. drove to the store or accessed a website, saved the information for later, etc. This analysis is also known as ad attribution. Comparing the activity of vehicles that viewed/heard an ad to the vehicles that did not view/hear the ad results in the ability to measure video store ad conversion rates, number of store visits, advertising lift and ad cost per store visit. Combining this data with consumer store spending data leads to a value per vehicle visit. Using GPS location data derived from the vehicle the system can develop driving patterns, store visit locations, and visited store types which comprise valuable intelligence for retailers. Such intelligence may include, for example, metrics describing the frequency, timing, number and type of visits/occurrences, repeating patterns, financial and other value exchange, redemptions, purchase analytics and the like. The impact may be measured using a variety of methods such as including both quantative and qualitative, as are typically employed in analysis of advertising, promotion and marketing campaigns, such as conversion factors, upsell analytics, engagement metrics (such as time in store) and the like which are well known in the art. Over time, visitation trends and macro and micro level events affecting real world behavior can be determined. Combining vehicle location data with consumer demographics, mobile devices and app usage delivers the most accurate ad targeting capability.
In addition to the aforementioned benefits, the instant system and methodology allows for the acknowledgement that information received from a vehicle entertainment system is partial in nature, in that such information does not convey the context of the experience of a vehicle's occupants for a media event. The combination of verifiable proof of performance, through accurate time and location alignment combined with multiple media sources and contextual information provides a rich, accurate and immutable record of a vehicles occupants experiences. The information sets for traditional methods, center on a single source, a vehicle's inbuilt radio, whereas modern vehicles today include entertainment systems that support, radio, other hard media such as CD, USB and the like and connected sources such as embedded wireless modems and smart phones. Even the purveyors of systems that track podcasts, one of the most rapidly growing media sources of today, state that listening behavior cannot be monitored.
The use of an immutable repository, such as a distributed ledger, to record the timing and location information is complemented by the use of cryptographically bound containers, which span a period of time, with a granularity that can differ from the underlying blocks of the distributed ledger to provide further benefits over the prior art systems.
The foregoing attempts in the prior art fail to meet the needs of the industry, and the various stakeholders thereof. There exists significant industry demand, from the stakeholders identified above, for a more comprehensive in-vehicle media consumption measurement system that can provide greater accuracy, finer granularity and real-time measurement/analysis of media content consumption across all applicable sources—such a system does not exist today.